MR imaging of left-ventricular function : novel image acquisition and analysis techniques.

Many cardiac diseases, such as myocardial ischemia, secondary to coronary artery disease, may be identified and localized through the analysis of cardiac deformations. Early efforts for quantifying ventricular wall motion used surgical implantation and tracking of radiopaque markers with X-ray imaging in canine hearts [1]. Such techniques are invasive and affect the regional motion pattern of the ventricular wall during the marker tracking process and, clearly are not feasible clinically. Noninvasive imaging techniques are vital and have been widely applied to the clinic. MRI is a noninvasive imaging technique with the capability to monitor and assess the progression of cardiovascular diseases (CVD) so that effective procedures for the care and treatment of patients can be developed by physicians and researchers. It is capable of providing 3D analysis of global and regional cardiac function with great accuracy and reproducibility. In the past few years, numerous efforts have been devoted to cardiac motion recovery and deformation analysis from MR imaging sequences. In order to assess cardiac function, there are two categories of indices that are used: global and regional indices. Global indices include ejection fraction, cavity volume, and myocardial mass [2]. They are important indices for cardiac disease diagnosis. However, these global indices are not specific for regional analysis. A quantitative assessment of regional parameters may prove beneficial for the diagnosis of disease and evaluation of severity and the quantification of treatment [3]. Local measures, such as wall deformation and strain in all regions of the heart, can provide objective regional quantification of ventricular wall function and relate to the location and extent of ischemic injury. This dissertation is concerned with the development of novel MR imaging techniques and image postprocessing algorithms to analyze left ventricular deformations. A novel pulse sequence, termed Orthogonal CSPAMM (OCSPAMM), has been proposed which results in the same acquisition time as SPAMM for 2D deformation estimation while keeping the main advantages of CSPAMM [4,5]: i.e., maintaining tag contrast through-out the ECG cycle. Different from CSPAMM, in OCSPAMM the second tagging pulse orientation is rotated 90 degrees relative to the first one so that motion information can be obtained simultaneously in two directions. This reduces the acquisition time by a factor of two as compared to the traditional CSPAMM, in which two separate imaging sequences are applied per acquisition. With the application of OCSPAMM, the effect of tag fading encountered in SPAMM tagging due to Tl relaxation is mitigated and tag deformations can be visualized for the entire cardiac cycle, including diastolic phases. A multilevel B-spline fitting method (MBS) has been proposed which incorporates phase-based displacement information for accurate calculation of 2D motion and strain from tagged MRI [6, 7]. The proposed method combines the advantages of continuity and smoothness of MBS, and makes use of phase information derived from tagged MR images. Compared to previous 2D B-spline-based deformation analysis methods, MBS has the following advantages: 1) It can simultaneously achieve a smooth deformation while accurately approximating the given data set; 2) Computationally, it is very fast; and 3) It can produce more accurate deformation results. Since the tag intersections (intersections between two tag lines) can be extracted accurately and are more or less distributed evenly over the myocardium, MBS has proven effective for 2D cardiac motion tracking. To derive phase-based displacements, 2D HARP and SinMod analysis techniques [8,9] were employed. By producing virtual tags from HARP /SinMod and calculating intersections of virtual tag lines, more data points are obtained. In the reference frame, virtual tag lines are the isoparametric curves of an undeformed 2D B-spline model. In subsequent frames, the locations of intersections of virtual tag lines over the myocardium are updated with phase-based displacement. The advantage of the technique is that in acquiring denser myocardial displacements, it uses both real and virtual tag line intersections. It is fast and more accurate than 2D HARP and SinMod tracking. A novel 3D sine wave modeling (3D SinMod) approach for automatic analysis of 3D cardiac deformations has been proposed [10]. An accelerated 3D complementary spatial modulation of magnetization (CSPAMM) tagging technique [11] was used to acquire complete 3D+t tagged MR data sets of the whole heart (3 dynamic CSPAMM tagged MRI volume with tags in different orientations), in-vivo, in 54 heart beats and within 3 breath-holds. In 3D SinMod, the intensity distribution around each pixel is modeled as a cosine wave front. The principle behind 3D SinMod tracking is that both phase and frequency for each voxel are determined directly from the frequency analysis and the displacement is calculated from the quotient of phase difference and local frequency. The deformation fields clearly demonstrate longitudinal shortening during systole. The contraction of the LV base towards the apex as well as the torsional motion between basal and apical slices is clearly observable from the displacements. 3D SinMod can automatically process the image data to derive measures of motion, deformations, and strains between consecutive pair of tagged volumes in 17 seconds. Therefore, comprehensive 4D imaging and postprocessing for determination of ventricular function is now possible in under 10 minutes. For validation of 3D SinMod, 7 3D+t CSPAMM data sets of healthy subjects have been processed. Comparison of mid-wall contour deformations and circumferential shortening results by 3D SinMod showed good agreement with those by 3D HARP. Tag lines tracked by the proposed technique were also compared with manually delineated ones. The average errors calculated for the systolic phase of the cardiac cycles were in the sub-pixel range

Cardiac image computing for myocardial infarction patients

Cardiovascular diseases (CVDs), which are a prime cause of global mortality, are disorders that affect the heart and blood vessels' functioning. CVDs may cause consequent complications, due to occlusion in a blood vessel and present as impaired cardiac wall functioning (myocardium). Identifying such impairment (infarction) of the myocardium is of great clinical interest, as it can reveal the nature of altered cardiac topography (ventricular remodelling) to aid the associated intervention decisions. With recent advances in cardiac imaging, such as Magnetic Resonance (MR) imaging, the visualisation and identification of infarcted myocardium has been routinely and effectively used in clinical practice. Diagnosing infarcted myocardium is achieved clinically through the late gadolinium enhancement (LGE) test, which acquires MR images after injecting a gadolinium-based contrast agent (GBCA). Due to the increased accuracy and reproducibility, LGE has emerged as the gold-standard MR imaging test in identifying myocardial infarction. However, clinical studies have reported gadolinium deposition concerns in different body organs and adverse outcomes in patients with advanced kidney failure, over time. Such incidents have motivated researchers to look into the development of both accurate as well as safe diagnostic tools. Emerging research on identifying infarcted myocardium utilises myocardial strain to safely identify infarcted myocardium, which has been addressed in the presented study. For example, myocardial strain represents the shortening or lengthening of the myocardium. If the myocardium is infarcted, then the corresponding strain values differ compared to the healthy myocardium. This finding can be identified and utilised for clinical applications. The research presented in this thesis aims to identify infarcted myocardium accurately and safely by using myocardial strain (shortening or lengthening of the myocardium). To achieve the aforementioned aim, the research methodology is divided into six objectives. The initial objectives relate to the development of a novel myocardial tracking method. The middle objectives relate to the development of clinical application methods, and the final objectives concern the validation of the developed methods through clinical studies and associated datasets. The research presented in this thesis has addressed the following research question: Research question 1: How can a 2D myocardial tracking and strain calculation method be developed using the 2D local weighted mean function and structural deformation within the myocardium? Research question 2: How can a 3D myocardial tracking and strain calculation method be developed using the 3D local weighted mean function to calculate 3D myocardial strain? Research question 3: How can 2D circumferential strain of the myocardium be used in identifying infarcted left ventricular segments for the diagnosis of myocardial infarction patients? In literature, myocardial tracking and strain calculation methods have limited extension to 3D and dependency on tissue material properties. Moreover, additional limitations, such as limited inclusion of structural deformation details within the myocardium, are found in the literature. Therefore, methods are likely to become subjective or numerically unstable during computation. Moreover, the inclusion of myocardial details with grid-tagging MRI, for structural deformation within the myocardium, is more realistic compared to cine MRI.   The aforementioned limitations are overcome by proposing a novel Hierarchical Template Matching method, which performs non-rigid image registration among grid-tagging MR images of a cardiac cycle. This is achieved by employing a local weighted mean transformation function. The proposed non-rigid image registration method does not require the use of tissue material properties. Grid-tagging MRI is used to capture wall function within the myocardium, and the local weighted mean function is used for numerical stability. The performance of the developed methods is evaluated with multiple error measures and with a benchmark framework. This benchmark framework has provided an open-access 3D dataset, a set of validation methods, and results of four leading methods for comparison. Validation methods include qualitative and quantitative methods. The qualitative assessment of outcomes and verified ground truth for the quantitative evaluation of results are followed from the benchmark framework paper (Tobon-Gomez, Craene, Mcleod, et al., 2013). 2D HTM method has reported the root mean square error of point tracking in left ventricular slices, which are the basal slice 0.31±0.07 mm, the upper mid-ventricular slice 0.37±0.06 mm, the mid-ventricular slice 0.41±0.05 mm, and the apical slice 0.32±0.08 mm. The mid-ventricular slice has a significantly higher 4% (P=0.05) mean root mean square error compared to the other slices. However, the other slices do not have a significant difference among them. Compared to the benchmark free form deformation method, HTM has a mean error of 0.35±0.05 mm, which is 17% (P=0.07, CI:[-0.01,0.35]) reduced to the free form deformation method. Our technical method has shown the 3D extension of HTM and a method without using material properties, which is advantageous compared to the methods which are limited to 2D or dependent on material properties. Moreover, the 3D HTM has demonstrated the use of 3D local weighted mean function in 3D myocardial tracking. While comparing to the benchmark methods, it was found that the median tracking error of 3D HTM is comparable to benchmark methods and has very few outliers compared to them. The clinical results are validated with LGE imaging. The quantitative error measure is the area under the curve (AUC) of sensitivity vs 1-specificity curve of the receiver operating characteristic (ROC) test. The achieved AUC value in detecting infarcted segments in basal, mid-ventricular, and apical slices are 0.85, 0.82, and 0.87, respectively. Calculating AUC with 95% confidence level, the confidence intervals of lower and upper mean AUC values in basal, mid-ventricular and apical slices are [0.80, 0.89], [0.74, 0.85], and [0.78, 0.91], respectively. Overall, considering the detections of LGE imaging as the base, our method has an accuracy of AUC 0.73 (P=0.05) in identifying infarcted left ventricular segments. The developed methods have shown, systematically, a promising approach in identifying infarcted left ventricular segments by image processing method and without using GBCA-based LGE imaging.

Joint Strain Analysis in cardia MRI

Master'sMASTER OF ENGINEERIN

Filter Design and Consistency Evaluation for 3D Tongue Motion Estimation using Harmonic Phase Analysis Method

Understanding patterns of tongue motion in speech using 3D motion estimation is challenging. Harmonic phase analysis has been used to perform noninvasive tongue motion and strain estimation using tagged magnetic resonance imaging (MRI). Two main contributions have been made in this thesis. First, the filtering process, which is used to produce harmonic phase images used for tissue tracking, influences the estimation accuracy. For this work, we evaluated different filtering approaches, and propose a novel high-pass filter for volumes tagged in individual directions. Testing was done using an open benchmarking dataset and synthetic images obtained using a mechanical model. Second, the datasets with inconsistent motion need to be excluded to yield meaningful motion estimation. For this work, we used a tracking-based method to evaluate the motion consistency between datasets and gave a strategy to identify the inconsistent dataset. Experiments including 2 normal subjects were done to validate our method. In all, the first work about 3D filter design improves the motion estimation accuracy and the second work about motion consistency test ensures the meaningfulness of the estimation results

On motion in dynamic magnetic resonance imaging: Applications in cardiac function and abdominal diffusion

La imagen por resonancia magnética (MRI), hoy en día, representa una potente herramienta para el diagnóstico clínico debido a su flexibilidad y sensibilidad a un amplio rango de propiedades del tejido. Sus principales ventajas son su sobresaliente versatilidad y su capacidad para proporcionar alto contraste entre tejidos blandos. Gracias a esa versatilidad, la MRI se puede emplear para observar diferentes fenómenos físicos dentro del cuerpo humano combinando distintos tipos de pulsos dentro de la secuencia. Esto ha permitido crear distintas modalidades con múltiples aplicaciones tanto biológicas como clínicas. La adquisición de MR es, sin embargo, un proceso lento, lo que conlleva una solución de compromiso entre resolución y tiempo de adquisición (Lima da Cruz, 2016; Royuela-del Val, 2017). Debido a esto, la presencia de movimiento fisiológico durante la adquisición puede conllevar una grave degradación de la calidad de imagen, así como un incremento del tiempo de adquisición, aumentando así tambien la incomodidad del paciente. Esta limitación práctica representa un gran obstáculo para la viabilidad clínica de la MRI. En esta Tesis Doctoral se abordan dos problemas de interés en el campo de la MRI en los que el movimiento fisiológico tiene un papel protagonista. Éstos son, por un lado, la estimación robusta de parámetros de rotación y esfuerzo miocárdico a partir de imágenes de MR-Tagging dinámica para el diagnóstico y clasificación de cardiomiopatías y, por otro, la reconstrucción de mapas del coeficiente de difusión aparente (ADC) a alta resolución y con alta relación señal a ruido (SNR) a partir de adquisiciones de imagen ponderada en difusión (DWI) multiparamétrica en el hígado.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields

Accurate cardiac motion estimation from medical images such as ultrasound is important for clinical evaluation. We present a novel regularisation layer for cardiac motion estimation that will be applied after image registration and demonstrate its effectiveness. The regularisation utilises a spatio-temporal model of motion, b-splines of Fourier, to fit to displacement fields from pairwise image registration. In the process, it enforces spatial and temporal smoothness and consistency, cyclic nature of cardiac motion, and better adherence to the stroke volume of the heart. Flexibility is further given for inclusion of any set of registration displacement fields. The approach gave high accuracy. When applied to human adult Ultrasound data from a Cardiac Motion Analysis Challenge (CMAC), the proposed method is found to have 10% lower tracking error over CMAC participants. Satisfactory cardiac motion estimation is also demonstrated on other data sets, including human fetal echocardiography, chick embryonic heart ultrasound images, and zebrafish embryonic microscope images, with the average Dice coefficient between estimation motion and manual segmentation at 0.82–0.87. The approach of performing regularisation as an add-on layer after the completion of image registration is thus a viable option for cardiac motion estimation that can still have good accuracy. Since motion estimation algorithms are complex, dividing up regularisation and registration can simplify the process and provide flexibility. Further, owing to a large variety of existing registration algorithms, such an approach that is usable on any algorithm may be useful

QUANTITATIVE ELASTICITY IMAGING AND SOFT TISSUE CHARACTERIZATION USING TAGGED MAGNETIC RESONANCE IMAGING

Ph.DDOCTOR OF PHILOSOPH

Feature based estimation of myocardial motion from tagged MR images

In the past few years we witnessed an increase in mortality due to cancer relative to mortality due to cardiovascular diseases. In 2008, the Netherlands Statistics Agency reports that 33.900 people died of cancer against 33.100 deaths due to cardiovascular diseases, making cancer the number one cause of death in the Netherlands [33]. Even if the rate of people affected by heart diseases is continually rising, they "simply don’t die of it", according to the research director Prof. Mat Daemen of research institute CARIM of the University of Maastricht [50]. The reason for this is the early diagnosis, and the treatment of people with identified risk factors for diseases like ischemic heart disease, hypertrophic cardiomyopathy, thoracic aortic disease, pericardial (sac around the heart) disease, cardiac tumors, pulmonary artery disease, valvular disease, and congenital heart disease before and after surgical repair. Cardiac imaging plays a crucial role in the early diagnosis, since it allows the accurate investigation of a large amount of imaging data in a small amount of time. Moreover, cardiac imaging reduces costs of inpatient care, as has been shown in recent studies [77]. With this in mind, in this work we have provided several tools with the aim to help the investigation of the cardiac motion. In chapters 2 and 3 we have explored a novel variational optic flow methodology based on multi-scale feature points to extract cardiac motion from tagged MR images. Compared to constant brightness methods, this new approach exhibits several advantages. Although the intensity of critical points is also influenced by fading, critical points do retain their characteristic even in the presence of intensity changes, such as in MR imaging. In an experiment in section 5.4 we have applied this optic flow approach directly on tagged MR images. A visual inspection confirmed that the extracted motion fields realistically depicted the cardiac wall motion. The method exploits also the advantages from the multiscale framework. Because sparse velocity formulas 2.9, 3.7, 6.21, and 7.5 provide a number of equations equal to the number of unknowns, the method does not suffer from the aperture problem in retrieving velocities associated to the critical points. In chapters 2 and 3 we have moreover introduced a smoothness component of the optic flow equation described by means of covariant derivatives. This is a novelty in the optic flow literature. Many variational optic flow methods present a smoothness component that penalizes for changes from global assumptions such as isotropic or anisotropic smoothness. In the smoothness term proposed deviations from a predefined motion model are penalized. Moreover, the proposed optic flow equation has been decomposed in rotation-free and divergence-free components. This decomposition allows independent tuning of the two components during the vector field reconstruction. The experiments and the Table of errors provided in 3.8 showed that the combination of the smoothness term, influenced by a predefined motion model, and the Helmholtz decomposition in the optic flow equation reduces the average angular error substantially (20%-25%) with respect to a similar technique that employs only standard derivatives in the smoothness term. In section 5.3 we extracted the motion field of a phantom of which we know the ground truth of and compared the performance of this optic flow method with the performance of other optic flow methods well known in the literature, such as the Horn and Schunck [76] approach, the Lucas and Kanade [111] technique and the tuple image multi-scale optic flow constraint equation of Van Assen et al. [163]. Tests showed that the proposed optic flow methodology provides the smallest average angular error (AAE = 3.84 degrees) and L2 norm = 0.1. In this work we employed the Helmholtz decomposition also to study the cardiac behavior, since the vector field decomposition allows to investigate cardiac contraction and cardiac rotation independently. In chapter 4 we carried out an analysis of cardiac motion of ten volunteers and one patient where we estimated the kinetic energy for the different components. This decomposition is useful since it allows to visualize and quantify the contributions of each single vector field component to the heart beat. Local measurements of the kinetic energy have also been used to detect areas of the cardiac walls with little movement. Experiments on a patient and a comparison between a late enhancement cardiac image and an illustration of the cardiac kinetic energy on a bull’s eye plot illustrated that a correspondence between an infarcted area and an area with very small kinetic energy exists. With the aim to extend in the future the proposed optic flow equation to a 3D approach, in chapter 6 we investigated the 3D winding number approach as a tool to locate critical points in volume images. We simplified the mathematics involved with respect to a previous work [150] and we provided several examples and applications such as cardiac motion estimation from 3-dimensional tagged images, follicle and neuronal cell counting. Finally in chapter 7 we continued our investigation on volume tagged MR images, by retrieving the cardiac motion field using a 3-dimensional and simple version of the proposed optic flow equation based on standard derivatives. We showed that the retrieved motion fields display the contracting and rotating behavior of the cardiac muscle. We moreover extracted the through-plane component, which provides a realistic illustration of the vector field and is missed by 2-dimensional approaches

In vivo assessment of the performance of strain-encoded MRI (DENSE) in healthy subjects and patients with myocardial infarction

Introduction: In patients with myocardial infarction (MI), regional left ventricular contractile function has important prognostic value. Displacement ENcoding with Stimulated Echoes (DENSE) is an MRI technique which has been developed to allow quantitative assessment of myocardial strain. To date, much of the research performed with DENSE has been methods development, and its application in a routine clinical setting has been incompletely investigated. The purpose of the research presented within this thesis was to investigate variations in strain within the healthy heart, and then to assess the in vivo performance of DENSE strain imaging in acute and chronic myocardial infarction (MI). Methods: 80 healthy subjects (M:F = 40:40, age 43 +/- 17 years) were recruited from the community. 50 male patients (age 56 +/- 10 years) were recruited from the clinical service and scanned within 7 days of myocardial infarction (“acute MI”), and invited to return for a follow-up scan after 6 months (“chronic MI”). MR imaging was performed on a 1.5T Siemens Avanto scanner, using an imaging protocol which included DENSE, cine, late gadolinium enhancement (LGE, patients only) and T2-weighted imaging acquired from left ventricular (LV) short-axis slices in both basal and mid-ventricular positions, which were divided into 6 segments for analysis. The percentage of LGE and T2 hyperenhancement within each segment were calculated, and the area at risk (acute MI) and myocardial salvage index (chronic MI) were determined. DENSE images were analysed to obtain values for strain parameters relating to circumferential strain (Ecc). Strain measurements obtained from healthy subjects were used to investigate the variations in Ecc with age, gender, slice position and myocardial segment. Strain measurements obtained from MI patients were used to investigate the relationships between Ecc and the extent of myocardial infarction, area at risk and/or salvage, and to determine whether DENSE strain measurements are informative in acute and chronic MI. Results: Comparison of DENSE strain measurements in healthy subjects revealed statistically significant differences between males and females, and between measurements obtained from basal and mid-ventricular short-axis slice positions. These differences must be taken into account to allow appropriate analysis of DENSE data in patients. DENSE was found to be informative in both acute and chronic MI. At both time points, strain measurements can be used to distinguish between myocardial segments with 0%, 50% infarction. There is the potential for the development of reference ranges which could be applied to strain measurements from future MI patients to allow assessment of the extent of infarction. In acute MI, four additional applications were identified: i) comparison with references ranges, established from strain measurements in healthy subjects, can be used to identify the presence of infarction with high specificity and moderate to high sensitivity, ii) peak Ecc can be used to distinguish between segments categorised as remote and adjacent, iii) strain measurements in the acute setting may provide prognostic information relating to the potential progression or recovery of contractile abnormalities in the chronic setting, iv) peak Ecc may allow a preliminary assessment of LV ejection fraction. Sensitivity for the detection of injured but non-infarcted segments was low. In chronic MI, two additional applications were identified: i) strain recovery can be detected in infarcted myocardial segments, and also in non-infarcted segments which are located adjacent to infarcted segments, which could improve identification of changes in contractile function compared to conventional qualitative analysis of cine imaging, ii) strain measurements can be used to distinguish between segments in which the extent of infarction has increased and those in which it has decreased. The relationships with myocardial salvage index were not found to be informative. Conclusions: DENSE images were successfully acquired and analysed from both healthy subjects and patients with myocardial infarction, which indicates that the technique is feasible in different clinical settings. DENSE strain measurements were found to be informative in both acute and chronic MI, and can provide insight into the presence and extent of infarction and the progression or recovery of contractile abnormalities

Role of deep learning techniques in non-invasive diagnosis of human diseases.

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis